基于模糊树模型的间接自适应模糊控制

提出了一种基于模糊树模型的间接自适应模糊控制器的设计方法. 采用模糊树辨识方法离线辨识系统中的未知非线性函数, 得到初始的控制器, 然后在线调节模糊树模型中的线性参数, 改善控制器的性能, 实现对有界参考信号的跟踪控制. 通过对倒立摆系统进行数值仿真, 验证了所提方法的有效性和优越性.     

一类非线性系统的积分变结构模糊自适应跟踪控制

针对一类具有未知常数控制增益的不确定非线性系统,基于变结构控制原理,并利用具有非线性可调参数的模糊系统逼近等价控制,提出一种具有监督控制器的积分变结构模糊自适应跟踪控制策略.该策略通过监督控制器保证闭环系统所有信号有界.进一步,通过引入最优逼近误差的自适应补偿项来消除建模误差的影响.理论分析证明了跟踪误差能够收敛到零.仿真结果表明了该方法的有效性.     

具有模糊监督控制器的积分变结构间接自适应控制

针对一类不确定非线性系统, 基于变结构控制原理, 并利用具有非线性可调参数的模糊系统去逼近过程未知函数, 提出一种具有模糊监督控制器的积分变结构间接自适应控制方案. 该方案通过监督控制器保证闭环系统所有信号有界. 进一步, 通过引入最优逼近误差的自适应补偿项来消除建模误差的影响. 理论分析证明了跟踪误差收敛到零. 仿真结果表明了该方法的有效性.     

控制方向未知的SISO非仿射系统间接自适应模糊输出反馈控制

针对一类单输入单输出(SISO)非仿射非线性系统控制方向未知时出现的控制器奇异问题,提出了一种间接自适应模糊控制方案.利用中值定理将非仿射系统转化为仿射系统,通过模糊逻辑系统逼近该仿射系统中的未知函数,并构造模糊控制器,同时利用Lyapunov稳定性定理设计自适应律,最终克服了控制器的奇异问题;在此基础上,通过构造观测器估计跟踪误差,设计输出反馈自适应模糊控制器,解决了状态不可测时系统控制器设计难题,采用Lyapunov稳定性定理证明控制器能使得跟踪误差收敛同时闭环系统所有信号均有界.仿真结果验证了所设计控制方案的可行性与有效性.     

不确定多输入非线性系统自适应模糊滑模控制器设计

针对一类不确定多输入非线性系统提出一种新的自适应模糊滑模控制器,该控制器在存在模型逻辑系统逼近误差的情况下使闭环系统跟踪误差小于预先给定常数,消除滑模控制中的抖振,缓解因系统维数增高所致的模糊规则爆炸现象,最后用仿算例验证了所提出方法的有效性。     

一种基于T-S模糊模型的混沌控制方法

用T-S模糊系统来逼近非线性系统,它的IF-THEN规则后件由线性状态空间子系统构成,进而可以应用模糊系统的控制理论求得模糊控制器,用此非线性控制器来控制非线性系统,以求良好的控制效果;将模糊控制技术应用于混沌控制中,可以克服反馈线性化等传统方法对参数完全精确已知的限制;模糊规则后件部分以局部线性方程形式给出的T-S模糊模型可以通过调整相关参数很好地逼近混沌系统,基于该模型采用平行分散补偿技术设计出具有相同规则数目的模糊控制器,控制器所有参数可以通过求解一组线性矩阵不等式一次性得到。仿真结果验证了该方法的有效性。     

输入受限四旋翼飞行器的模糊自适应动态面轨迹跟踪控制

针对输入受限条件下四旋翼飞行器的轨迹跟踪控制问题,考虑系统存在模型动态不确定和未知外界干扰的情况,提出一种模糊自适应动态面轨迹跟踪控制方法.该方法设计干扰观测器估计位置模型中复合扰动项,利用模糊系统逼近姿态模型中不确定项和外界干扰,并引入双曲正切函数和辅助系统处理输入受限问题,结合反演法和动态面技术设计轨迹跟踪控制器,以降低控制算法的复杂性,最后选取李雅普诺夫函数证明闭环系统所有信号一致最终有界.应用大疆M100飞行器模型进行仿真验证,结果表明所设计的控制器能够有效处理模型动态不确定和未知外界干扰问题,避免飞行器工作过程中因输入饱和导致执行器失效现象,精确地完成轨迹跟踪控制任务.     

基于量子遗传算法的模糊滑模控制

针对一类具有不确定性的非线性系统,提出了一种新的基于量子遗传算法的模糊滑模控制器的设计方法.将模糊控制与滑模控制相结合,利用滑模控制使系统跟踪误差进入边界层内;启用模糊控制替代切换控制,并在边界层上通过监督函数平滑控制作用.在滑动模态产生条件下,通过量子遗传算法优化模糊控制器的控制规则,有效地解决了模糊滑模控制中模糊控制规则的确定问题,从而削弱了系统的抖振,改善了控制器的控制性能.仿真结果表明了该方法的有效性.     

输入受限的非线性系统自适应模糊backstepping控制

针对一类输入受限的非线性系统,提出了一种自适应模糊backsteppig控制器的设计方法.在控制器的设计过程当中,采用模糊系统对不确定非线性函数在线逼近;利用双曲正切函数和Nussbaum函数对系统输入饱和函数进行处理;将动态面法与backstepping法相结合解决"计算膨胀"的问题.通过Lyapunov理论分析证明了所设计的控制器能够使闭环系统所有信号半全局一致有界(SGUUB).最后应用于高超声速飞行器的攻角跟踪控制中,仿真结果表明该方法的有效性.     

基于模糊树模型的自适应直接逆控制

基于模糊树模型, 结合神经网络中的逆向学习和专门化学习, 提出了自适应直接逆控制方法. 首先离线辨识对象的逆模型作为初始的控制器, 然后与对象串联, 用最小均方差 (Least mean square, LMS) 算法在线调节控制器中的线性参数. 本方法辨识得到的逆模型控制器可以减少需要的模糊规则数目, 同时达到较好的跟踪控制效果. 仿真结果表明了方法的有效性.     

基于T-S模糊模型的非线性预测控制策略

提出了一种新的基于T-S模糊模型的非线性预测控制策略. T-S模糊模型用于描述对象的非线性动态特性, 通过将模糊模型的输出反馈回来作为模型输入, 从而构成了模糊多步预报器. 由于T-S模糊模型每条规则的结论部分是一个线性模型, 因此整个模糊模型可以看作一个线性时变系统, 从而将模糊预测控制器中的非线性优化问题转化为一个线性二次寻优问题, 以方便求解. pH中和过程的仿真结果表明其性能优于传统的动态矩阵控制器.     

Adaptive sliding mode controller design based on T-S fuzzy system models

An adaptive sliding mode control (ASMC) technique based on T-S fuzzy system models is proposed in this paper for a class of perturbed nonlinear MIMO dynamic systems in order to solve tracking problems. A T-S fuzzy model is firstly formed by utilizing fuzzy theorem to amalgamate a set of linearized dynamic equations. The adaptive sliding mode controller is then designed based on this fuzzy model with perturbations. The proposed control scheme can drive the dynamics of controlled system into a designated sliding surface in finite time, and guarantee the property of asymptotical stability. It is also shown that the information of upper bound of modeling errors as well as perturbations, except the information of upper bound of input uncertainty, is not required when using the proposed controller.     

Adaptive fuzzy identification and predictive control for industrial processes

This paper proposes a method for adaptive identification and control for industrial applications. The learning of a T–S fuzzy model is performed from input/output data to approximate unknown nonlinear processes by a hierarchical genetic algorithm (HGA). The HGA approach is composed by five hierarchical levels where the following parameters of the T–S fuzzy system are learned: input variables and their respective time delays, antecedent fuzzy sets, consequent parameters, and fuzzy rules. In order to reduce the computational cost and increase the algorithm’s performance an initialization method is applied on HGA. To deal with nonlinear plants and time-varying processes, the T–S fuzzy model is adapted online to maintain the quality of the identification/control. The identification methodology is proposed for two application problems: (1) the design of data-driven soft sensors, and (2) the learning of a model for the Generalized predictive control (GPC) algorithm. The integration of the proposed adaptive identification method with the GPC results in an effective adaptive predictive fuzzy control methodology. To validate and demonstrate the performance and effectiveness of the proposed methodologies, they are applied on identification of a model for the estimation of the flour concentration in the effluent of a real-world wastewater treatment system; and on control of a simulated continuous stirred tank reactor (CSTR) and on a real experimental setup composed of two coupled DC motors. The results are presented, showing that the developed evolving T–S fuzzy model can identify the nonlinear systems satisfactorily and it can be used successfully as a prediction model of the process for the GPC controller.     

Direct adaptive fuzzy control for nonlinear systems with time-varying delays

This paper focuses on the problem of direct adaptive fuzzy control for nonlinear strict-feedback systems with time-varying delays. Based on the Razumikhin function approach, a novel adaptive fuzzy controller is designed. The proposed controller guarantees that the system output converges to a small neighborhood of the reference signal and all the signals in the closed-loop system remain bounded. Different from the existing adaptive fuzzy control methodology, the fuzzy logic systems are used to model the desired but unknown control signals rather than the unknown nonlinear functions in the systems. As a result, the proposed adaptive controller has a simpler form and requires fewer adaptation parameters.     

Approximation-based adaptive fuzzy control for a class of non-strict-feedback stochastic nonlinear systems

This paper considers the problem of adaptive fuzzy control of a class of single-input/single-output （SISO） nonlinear stochastic systems in non-strict-feedback form. Fuzzy logic systems are used to approximate the uncertain nonlinearities and backstepping technique is utilized to construct an adaptive fuzzy controller. The proposed controller guarantees that all the signals in the resulting closed-loop system are bounded in probability. The main contribution of this note lies in providing a control strategy for a class of nonlinear systems in non- strict-feedback form. Simulation result is used to test the effectiveness of the suggested approach.     

Adaptive fuzzy dynamic surface control for uncertain nonlinear systems

In this paper, a robust adaptive fuzzy dynamic surface control for a class of uncertain nonlinear systems is proposed. A novel adaptive fuzzy dynamic surface model is built to approximate the uncertain nonlinear functions by only one fuzzy logic system. The approximation capability of this model is proved and the model is implemented to solve the problem that too many approximators are used in the controller design of uncertain nonlinear systems. The shortage of ＂explosion of complexity＂ in backstepping design procedure is overcome by using the proposed dynamic surface control method. It is proved by constructing appropriate Lyapunov candidates that all signals of closed-loop systems are semi-globally uniformly ultimate bounded. Also, this novel controller stabilizes the states of uncertain nonlinear systems faster than the adaptive sliding mode controller （SMC）. Two simulation examples are provided to illustrate the effectiveness of the control approach proposed in this paper.     

无级变速汽车的自适应模糊控制研究

为提高无级变速汽车控制系统对外界负荷扰动和各种运行工况的自适应能力,本文在简单的无级变速系统模型的基础上,设计了无级变速汽车的自适应模糊控制系统.所设计的自适应模糊控制系统由参数自调节油门模糊控制器和混合型模糊-PID的无级变速器速比和制动控制器构成,该控制系统只与汽车的瞬时运行状态有关而与汽车的参数无关,因此它对不确定的汽车参数和外界负荷的扰动具有良好的鲁棒性.仿真结果表明所设计的模糊控制系统具有良好的自适应能力.     

Stable nonlinear controller design for a Takagi-Sugeno fuzzy model

This paper proposes another adaptive control scheme for nonlinear systems using a Takagi-Sugeno fuzzy model. Takagi-Sugeno fuzzy models have been widely used to identify the structures and parameters of unknown or partially known plants, and to control nonlinear systems. This scheme shows a good approximation capability by the fuzzy blending of local dynamics. Since a Takagi-Sugeno fuzzy model is a nonlinear system in nature, and its parameters are not linearly parameterized, it is difficult to design an adaptive controller using conventional design methods for adaptive controllers which are derived from linearly parameterized systems. In this paper, the functional form of the local dynamics are assumed to be known, but the corresponding parameters are unknown. This additional information about system nonlinearity makes it possible to design an adaptive controller for a nonlinearly parameterized system. The control law is similar to that of a conventional adaptive control technique, while its parameter-update rule is based on the local search method. A parameter-update law is derived so that the time-derivative of the Lyapunov function is negative in the region of interest. Simulation results have shown that this adaptive controller is capable of a good performance. This work was presented in part at the Fifth International Symposium on Artificial Life and Robotics, Oita, Japan, January 26–28, 2000     

非线性模糊时滞系统鲁棒自适应控制

研究一类基于模糊T-S模型的非线性时滞系统鲁棒镇定问题．基于记忆型状态反馈策略，首先给出由T-S模糊模型描述的非线性时滞系统在时滞精确已知情况下的鲁棒镇定准则；然后给出非线性时滞系统在时滞未知情况下的鲁棒自适应控制策略．所设计的控制器可确保闭环系统渐近稳定，且具有良好的可操作性．最后通过仿真实例证明了该方法的正确性和有效性．